





"""simple docstring"""





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = ['input_features']





      def __init__(			self ,		lowercase=80 ,		lowercase=16_000 ,		lowercase=160 ,		lowercase=30 ,		lowercase=400 ,		lowercase=0.0 ,		lowercase=False ,		**lowercase ,		)						->	Union[str, Any]:
            super().__init__(
                feature_size=lowercase ,		sampling_rate=lowercase ,		padding_value=lowercase ,		return_attention_mask=lowercase ,		**lowercase ,		)
            lowerCAmelCase    		=			n_fft
            lowerCAmelCase    		=			hop_length
            lowerCAmelCase    		=			chunk_length
            lowerCAmelCase    		=			chunk_length * sampling_rate
            lowerCAmelCase    		=			self.n_samples // hop_length
            lowerCAmelCase    		=			sampling_rate
            lowerCAmelCase    		=			mel_filter_bank(
                num_frequency_bins=1 + n_fft // 2 ,		num_mel_filters=lowercase ,		min_frequency=0.0 ,		max_frequency=8_000.0 ,		sampling_rate=lowercase ,		norm="""slaney""" ,		mel_scale="""slaney""" ,		)





      def        _snake_case     (			self ,		lowercase					)						->	np.ndarray:
            lowerCAmelCase    		=			spectrogram(
                lowercase ,		window_function(self.n_fft ,		"""hann"""					) ,		frame_length=self.n_fft ,		hop_length=self.hop_length ,		power=2.0 ,		mel_filters=self.mel_filters ,		log_mel="""log10""" ,		)
            lowerCAmelCase    		=			log_spec[:, :-1]
            lowerCAmelCase    		=			np.maximum(lowercase ,		log_spec.max() - 8.0					)
            lowerCAmelCase    		=			(log_spec + 4.0) / 4.0
            return log_spec





      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def        _snake_case     (			lowercase ,		lowercase ,		lowercase = 0.0					)						->	List[np.ndarray]:
            if attention_mask is not None:
                  lowerCAmelCase    		=			np.array(lowercase ,		np.intaa					)
                  lowerCAmelCase    		=			[]

                  for vector, length in zip(lowercase ,		attention_mask.sum(-1					)					):
                        lowerCAmelCase    		=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7					)
                        if length < normed_slice.shape[0]:
                              lowerCAmelCase    		=			padding_value

                        normed_input_values.append(lowercase					)
            else:
                  lowerCAmelCase    		=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7					) for x in input_values]

            return normed_input_values





      def __call__(			self ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = "max_length" ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	BatchFeature:

            if sampling_rate is not None:
                  if sampling_rate != self.sampling_rate:
                        raise ValueError(
                            f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
                            f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
                            f' was sampled with {self.sampling_rate} and not {sampling_rate}.'					)
            else:
                  logger.warning(
                      """It is strongly recommended to pass the `sampling_rate` argument to this function. """
                      """Failing to do so can result in silent errors that might be hard to debug."""					)

            lowerCAmelCase    		=			isinstance(lowercase ,		np.ndarray					) and len(raw_speech.shape					) > 1
            if is_batched_numpy and len(raw_speech.shape					) > 2:
                  raise ValueError(f'Only mono-channel audio is supported for input to {self}'					)
            lowerCAmelCase    		=			is_batched_numpy or (
                isinstance(lowercase ,		(list, tuple)					) and (isinstance(raw_speech[0] ,		(np.ndarray, tuple, list)					))
            )

            if is_batched:
                  lowerCAmelCase    		=			[np.asarray([speech] ,		dtype=np.floataa					).T for speech in raw_speech]
            elif not is_batched and not isinstance(lowercase ,		np.ndarray					):
                  lowerCAmelCase    		=			np.asarray(lowercase ,		dtype=np.floataa					)
            elif isinstance(lowercase ,		np.ndarray					) and raw_speech.dtype is np.dtype(np.floataa					):
                  lowerCAmelCase    		=			raw_speech.astype(np.floataa					)

            # always return batch
            if not is_batched:
                  lowerCAmelCase    		=			[np.asarray([raw_speech]					).T]

            lowerCAmelCase    		=			BatchFeature({"""input_features""": raw_speech}					)

            # convert into correct format for padding

            lowerCAmelCase    		=			self.pad(
                lowercase ,		padding=lowercase ,		max_length=max_length if max_length else self.n_samples ,		truncation=lowercase ,		pad_to_multiple_of=lowercase ,		return_attention_mask=return_attention_mask or do_normalize ,		)

            # zero-mean and unit-variance normalization
            if do_normalize:
                  lowerCAmelCase    		=			self.zero_mean_unit_var_norm(
                      padded_inputs["""input_features"""] ,		attention_mask=padded_inputs["""attention_mask"""] ,		padding_value=self.padding_value ,		)
                  lowerCAmelCase    		=			np.stack(padded_inputs["""input_features"""] ,		axis=0					)

            # make sure list is in array format
            lowerCAmelCase    		=			padded_inputs.get("""input_features"""					).transpose(2 ,		0 ,		1					)

            lowerCAmelCase    		=			[self._np_extract_fbank_features(lowercase					) for waveform in input_features[0]]

            if isinstance(input_features[0] ,		lowercase					):
                  lowerCAmelCase    		=			[np.asarray(lowercase ,		dtype=np.floataa					) for feature in input_features]
            else:
                  lowerCAmelCase    		=			input_features

            if return_attention_mask:
                  # rescale from sample (48000) to feature (3000)
                  lowerCAmelCase    		=			padded_inputs["""attention_mask"""][:, :: self.hop_length]

            if return_tensors is not None:
                  lowerCAmelCase    		=			padded_inputs.convert_to_tensors(lowercase					)

            return padded_inputs





      def        _snake_case     (			self					)						->	Dict[str, Any]:
            lowerCAmelCase    		=			copy.deepcopy(self.__dict__					)
            lowerCAmelCase    		=			self.__class__.__name__
            if "mel_filters" in output:
                  del output["mel_filters"]
            return output













"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






